Graph-Based Keyphrase Extraction for Software Traceability in Source Code and Documentation Mapping

Abstract

Natural Language Processing (NLP) forms the basis of several computational tasks. However,  when applied to the software system’s, NLP provides several irrelevant features and the noise gets mixed up while extracting features. As the scale of software system’s increases,   different   metrics are needed to assess these systems. Diagrammatic and visual representation of the SE projects code forms an essential component of Source Code Analysis (SCA). These SE projects cannot be analyzed by traditional source code analysis methods nor can they be analyzed by traditional diagrammatic representation. Hence, there is a need to modify the traditional approaches in lieu of changing environments to reduce learning gap for the developers and traceability engineers. The traditional approaches fall short in addressing specific metrics in terms of document similarity and graph dependency approaches. In terms of source code analysis, the graph dependency graph can be used for finding the relevant key-terms and keyphrases as they occur not just intra-document but also inter-document. In this work, a similarity measure based on context is proposed which can be employed to find a traceability link between the source code metrics and API documents present in a package.   Probabilistic graph-based keyphrase extraction approach is used for searching across the different project files.